Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical credentials, and fit within existing systems and information architecture
Lead communities of practice across Software Engineering to drive awareness and adoption of new and leading-edge technologies
Contribute to a team culture of diversity, equity, inclusion, and respect
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required Qualifications, Capabilities, and Skills
Formal training or certification in software engineering concepts
Hands-on experience delivering system design, application development, testing, and operational stability
Advanced programming capability in Python
Strong analytical and problem-solving skills
Experience with object-oriented programming
Proficiency in automation and continuous delivery methods
Advanced understanding of agile methodologies, including continuous integration and delivery, application resiliency, and security practices
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile)
Practical cloud-native experience
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred Qualifications, Capabilities, and Skills
Knowledge of AWS data sources
Experience with SecDB, Athena, or Quartz platforms
Macro trading business knowledge
UI experience with React.js and Node.js
Experience building microservices and distributed computing solutions
Knowledge of data modeling, performance engineering, and regression testing
